README.md
In pat-alt/SVAA: Deep Vector Autoregression

[!WARNING] I worked on this code base during my master's project in 2021 and today it is no longer actively maintained. Since the TensorFlow interface uses reticulate, you may run into compatibility issues with Python dependencies (see #4). While these things should be salvageable and I welcome contributions through pull requests, I have no capacity to work on this package myself.

[!NOTE] This is the repository for the companion package to our paper @altmeyer2021deep. We recently presented our paper at the NeurIPS 2021 MLECON workshop. For the specific code used for the paper and additional resources please see this repository.

`deepvars`

The deepvars package provides a framework for Deep Vector Autoregression in R. The methodology is based on (Altmeyer, Agusti, and Vidal-Quadras Costa 2021), a working paper initially prepared as part of the Masters Degree in Data Science at Barcelona School of Economics. For a summary of the first version of the working paper see here.

As one of its dependencies the deepvars uses tensorflow, which is an R interface to the popular TensorFlow library. We have tried to automate the TensorFlow configuration as explained here.

install.packages("tensorflow")
tensorflow::install_tensorflow()

For uncertainty quantification we use tensorflow_probability for Bayesian inference.

install.packages("tfprobability")
tfprobability::install_tfprobability()

Should you run into issues you may have to manually install the TensorFlow dependencies. Detailed instructions to this end can be found here.

You can either clone this repository and install from source or simply run the below in R:

devtools::install_github("pat-alt/deepvars", build_vignettes=TRUE)
library(deepvars)

Full documentation of the package is still a work-in-progress. In the meantime, detailed guidance on different topics and estimation methods covered by deepvars, can be found in the vignettes. Simply type the following command once you have completed the steps above: